Genetic evidence and integration of various data sources for classifying uncertain variants into a single model.
Genetic testing often results in the finding of a variant whose clinical significance is unknown. A number of different approaches have been employed in the attempt to classify such variants. For some variants, case-control, segregation, family history, or other statistical studies can provide strong evidence of direct association with cancer risk. For most variants, other evidence is available that relates to properties of the protein or gene sequence. In this work we propose a Bayesian method for assessing the likelihood that a variant is pathogenic. We discuss the assessment of prior probability, and how to combine the various sources of data into a statistically valid integrated assessment with a posterior probability of pathogenicity. In particular, we propose the use of a two-component mixture model to integrate these various sources of data and to estimate the parameters related to sensitivity and specificity of specific kinds of evidence. Further, we discuss some of the issues involved in this process and the assumptions that underpin many of the methods used in the evaluation process.
Goldgar
David E
DE
Department of Dermatology, University of Utah School of Medicine, Salt Lake City, Utah 84132-2409, USA. david.goldgar@hsc.utah.edu
Easton
Douglas F
DF
Byrnes
Graham B
GB
Spurdle
Amanda B
AB
Iversen
Edwin S
ES
Greenblatt
Marc S
MS
D001499:Bayes Theorem; D016022:Case-Control Studies; D003625:Data Collection; D020022:Genetic Predisposition to Disease; D005820:Genetic Testing / Q000379:methods*; D014644:Genetic Variation; D005838:Genotype; D006801:Humans; D016013:Likelihood Functions; D008954:Models, Biological; D009386:Neoplastic Syndromes, Hereditary / Q000145:classification* / Q000235:genetics; D010641:Phenotype; D012307:Risk Factors; D012680:Sensitivity and Specificity; D035501:Uncertainty